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关于支持向量分类机算法的研究 被引量：6

The Research about Algorithms of SVM Classification Methods
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摘要 研究分析了标准的支持向量机(C-SVM)、v支持向量机(v-SVM)等五种算法,利用仿真实验从分类精度,计算效率,扩展性等五个方面对上述五种算法进行了分析比较。 In this paper, comparison analyses are made of five algorithms methods including Standard Support Vector Machine （ C-SVM ） , v Support Vector Machine （ v-SVM ） and so on. Comparisons are made among the five methods through simulation examples in five aspects including the classification accuracy, the computing efficiency and the expansibility, etc.
作者 范玉妹 赵丽丽
机构地区 北京科技大学数力系
出处 《石家庄铁道学院学报》 2007年第3期31-36,共6页 Journal of Shijiazhuang Railway Institute
关键词 分类 支持向量机(SVM) C-SVM v-SVM WSVM PSVM LS-SVM classification Support Vector Machine （SVM） C-SVM v-SVM WSVM PSVM LS-SVM
分类号 O22 [理学—运筹学与控制论]
  • 相关文献

参考文献12

  • 1Huang Yimin,Du Shuxin.Weighted support vector machine for classification with uneven training class sizes[C]//Proceedings of the Fourth International Conference on Machine Learning and Cyberneties.New York:[s.n.],2005:18-21.
  • 2李东晖,杜树新,吴铁军.一种加权支持向量机方法及其应用[C] //第五届全球智能控制与自动化大会.杭州:[出版者不详],2004:1834-1837.
  • 3刘爽,贾传莹.一种加权支持向量机算法[C]//郑继禹,仇洪冰,王春宁.通信理论与信号处理新进展--2005年通信理论与信号处理年会论文集.北京:电子工业出版社,2005.
  • 4马云潜,张学工.支持向量机函数拟合在分形插值中的应用[J].清华大学学报（自然科学版）,2000,40(3):76-78. 被引量：32
  • 5庄东,陈英.基于加权近似支持向量机的文本分类[J].清华大学学报（自然科学版）,2005,45(S1):1787-1790. 被引量：16
  • 6Suykens J,Vandewalle J.Least square support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293-300.
  • 7Suykens J A K,Brabanter J Se,Lukas L,et al.Weighted least squares support vector machines:robustness and sparse approximation[J].Neurocomputing,2002,48:85-105.
  • 8陈毅松,汪国平,董士海.基于支持向量机的渐进直推式分类学习算法[J].软件学报,2003,14(3):451-460. 被引量：88
  • 9Lu Shuxia,Wang Xizhao.A comparison among four SVM classification methods:LSVM,NLSVM,SSVM and NSVM[C].Proceedings of the Third International Conference on Machine Learning and Cybernetics.New York:[s.n.],2004:4277-4282.
  • 10Qing Song,Hu Wenjie,Xie Wenfang.Robust support vector machine with bullet hole image classification[J].IEEE transactions on systems,man,and cybernetics-paric:applications and reviews,2002,32(4):440-448.

二级参考文献22

  • 1[1]Vapnik V. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
  • 2[2]Stitson MO, Weston JAE, Gammerman A, Vovk V, Vapnik V. Theory of support vector machines. Technical Report, CSD-TR-96-17, Computational Intelligence Group, Royal Holloway: University of London, 1996.
  • 3[3]Cortes C, Vapnik V. Support vector networks. Machine Learning, 1995,20:273～297.
  • 4[4]Vapnik V. Statistical Learning Theory. John Wiley and Sons, 1998.
  • 5[5]Gammerman A, Vapnik V, Vowk V. Learning by transduction. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Wisconsin, 1998. 148～156.
  • 6[6]Joachims T. Transductive inference for text classification using support vector machines. In: Proceedings of the 16th International Conference on Machine Learning (ICML). San Francisco: Morgan Kaufmann Publishers, 1999. 200～209.
  • 7[7]Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. In: Haussler D, ed. Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory. Pittsburgh, PA: ACM Press, 1992. 144～152.
  • 8[8]Burges CJC. Simplified support vector decision rules. In: Saitta L, ed. Proceedings of the 13th International Conference on Machine Learning. San Mateo, CA: Morgan Kaufmann Publishers, 1996. 71～77.
  • 9[9]Osuna E, Freund R, Girosi F. An improved training algorithm for support vector machines. In: Proceedings of the IEEE NNSP'97. Amelia Island, FL, 1997. 276～285.
  • 10[10]Joachims T. Making large-scale SVM learning practical. In: Scholkopf, Burges C, Smola A, eds. Advances in Kernel Methods--Support Vector Learning B. MIT Press, 1999.

共引文献211

同被引文献47

引证文献6

二级引证文献21

石家庄铁道学院学报
2007年 第3期

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